Intelligent Vehicle Monitoring for Safety and Security 2019-01-0129

The challenges posed by connected and autonomous vehicles fall beyond the scope of current version of ISO 26262. According to the current functional safety standard, controllability, largely affected by human intervention, is a large contributor to the definition of the Automotive Safety Integrity Level (ASIL). Since the driver involvement in CAVs will decrease in future, this gives no clear definition for future functional safety design. On the other hand, CAVs bring additional capabilities such as advance sensors, telematics-based connectivity etc. which can be used to devise efficient approaches to address functional safety (FuSa) challenges. The caveat to these additional capabilities is issues like cybersecurity, complexity, etc. This paper is an exploration into FuSa and CAVs and will present a systematic approach to understand challenges and propose potential framework, Intelligent Vehicle Monitoring for Safety and Security (IVMSS) to handle faults/malfunctions in CAVs, and specifically autonomous systems. Autonomous algorithms may be model-based for the on-board systems (e.g. motors, sensors etc.) or machine learning based algorithms can be used to diagnose the processes` malfunction (E.g. lane changing, overtaking, following the speed limits, parking, etc.) in CAVs. The goal of the framework is to deal with the functional safety challenges when the driver is not in the feedback loop. The additional set of sensors and connectivity can be used to develop IVMSS algorithms. With the advent of connected vehicles security also needs to be assessed, in fact it will be shown that safety is a subset of security. These threats can cause great risk to safety critical systems and thus be explored as well. The framework will build upon the lessons learned from current L2 technology out on the roads.